In recent years, the artificial intelligence has made a breakthrough in image identification. In order to find out the practical value of artificial intelligence models in radar echo nowcasting in Wuhan City, the radar echo and precipitation observation data in Wuhan from 2015 to 2020 are used to train four deep learning models (PredRNN++, MIM, CrevNet and PhyDNet), then these trained models and radar echo observation data in flood season of 2021 are used to do nowcasting of radar echo. And on this basis, the precipitation processes are selected by using precipitation intensity and area indexes in Wuhan, and the performance of four deep learning models and optical flow method in radar echo nowcasting are tested and evaluated in Wuhan in flood season of 2021 in terms of mean square error (MSE), structural similarity index measurement (SSIM), probability of detection (POD), false alarm rate (FAR) and critical success index (CSI). The results are as follows: (1) On the whole, MSE of MIM model is the smallest, while its POD is the highest, and SSIM of MIM and PredRNN++ models are the highest. FAR of four deep learning models is lower than that of optical flow method, and it is the lowest for PhyDNet model. Except for CrevNet model, CSI of other three deep learning models is higher than that of optical flow method, and it is the highest for MIM model. (2) CSI of optical flow method is the highest during 0-12 minutes of forecast, while that of MIM model is the highest from 18 to 120 minutes, which shows the advantage of deep learning model for long prediction time. (3) With the increase of echo intensity, POD and CSI of four deep learning models and optical flow method decrease rapidly, while the variation characteristics of FAR of optical flow method and deep learning models are different. (4) For the regional precipitation processes, the prediction ability of deep learning models firstly reduces and then enhances significantly with the increase of precipitation intensity, while the optical flow method is insensitive to the change of precipitation intensity, so the increments of CSI of deep learning models are the highest under the strong precipitation processes compared with optical flow method. For the local convective precipitation processes with general intensity, the prediction ability of all models and optical flow method significantly reduces. (5) The analysis results of a rainstorm case show that deep learning models not only have prediction ability to the change of echo intensity to a certain extent, but also have better prediction ability to echo movement than optical flow method, so they have a good operational prospect.

Based on daily meteorological observation data in main apple producing areas of China from 1971 to 2017, the crop coefficients at different growth stages of apple were corrected, firstly. And on this basis the precipitation suitability model at each growth stage of apple was constructed. Then, combined with the geographical distribution of apple in advantageous areas, the threshold of precipitation suitability of apple, and the spatial and temporal distribution characteristics of precipitation suitability at each growth stage of apple were analyzed. Furthermore, the temporal and spatial anomalies were discussed by using empirical orthogonal function (EOF) decomposition method. The results are as follows: (1) The thresholds of precipitation suitability at initial growth stage, vigorous growth stage and later growth stage of apple were 0.30-1.29, 0.63-1.78 and 0.62-2.84, respectively. The areas within the threshold range at each growth stage of apple accounted for 94.8%, 94.7% and 95.9% of main producing areas, respectively. The change trend of precipitation suitability in most regions at three growth stages of apple wasn’t significant from 1971 to 2017. (2) The variance contribution rates of the first eigenvector field of precipitation suitability at initial growth stage and vigorous growth stage of apple were 50.53% and 32.26%, respectively. The eigenvalues were almost positive in the whole region, which indicated that the spatial change of precipitation suitability had good consistency, and the oscillation intensity of precipitation suitability strengthened from northeast and southwest to the middle. The variance contribution rate of the first eigenvector field of precipitation suitability reached 49.51% at later growth period, and the distribution pattern in Liaoning Province and the local part of eastern Hebei Province was opposite to other areas. (3) The second eigenvector field of precipitation suitability appeared an opposite phase distribution pattern in the east and the west at initial growth period of apple, while that were anti-phase distribution pattern in northern and southern parts at vigorous growth period and later growth period of apple.